Vision-based vanishing point detection of autonomous navigation of mobile robot for outdoor applications

The vision-based approach to mobile robot navigation is considered superior due to its affordability. This paper aims to design and construct an autonomous mobile robot with a vision-based system for outdoor navigation. This robot receives inputs from camera and ultrasonic sensor. The camera is used to detect vanishing points and obstacles from the road. The vanishing point is used to detect the heading of the road. Lines are extracted from the environment using a canny edge detector and Houghline Transforms from OpenCV to navigate the system. Then, removed lines are processed to locate the vanishing point and the road angle. A low pass filter is then applied to detect a vanishing point better. The robot is tested to run in several outdoor conditions such as asphalt roads and pedestrian roads to follow the detected vanishing point. By implementing a Simple Blob Detector from OpenCV and ultrasonic sensor module, the obstacle's position in front of the robot is detected. The test results show that the robot can avoid obstacles while following the heading of the road in outdoor environments. Vision-based vanishing point detection is successfully applied for outdoor applications of autonomous mobile robot navigation.

Realtime autonomous navigation in V-Rep based static and dynamic environment using EKF-SLAM

<p>Localization in an autonomous mobile robot allows it to operate autonomously in an unknown and unpredictable environment with the ability to determine its position and heading. Simultaneous localization and mapping (SLAM) are introduced to solve the problem where no prior information about the environment is available either static or dynamic to achieve standard map-based localization. The primary focus of this research is autonomous mobile robot navigation using the extended Kalman filter (EKF)-SLAM environment modeling technique which provides higher accuracy and reliability in mobile robot localization and mapping results. In this paper, EKF-SLAM performance is verified by simulations performed in a static and dynamic environment designed in V-REP i.e., 3D Robot simulation environment. In this work SLAM problem of two-wheeled differential drive robot i.e., Pioneer 3-DX in indoor static and dynamic environment integrated with Laser range finder i.e., Hokuyo URG-04LX- UG01, LIDAR, and Ultrasonic sensors is solved. EKF-SLAM scripts are developed using MATLAB that is linked to V-REP via remote API feature to evaluate EKF-SLAM performance. The reached results confirm the EKF- SLAM is a reliable approach for real-time autonomous navigation for mobile robots in comparison to other techniques.</p>

An Efficient and Robust Improved A* Algorithm for Path Planning

Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the conventional A* algorithm and the subsequent improved algorithms still have some limitations in terms of robustness and efficiency. These limitations include slow algorithm efficiency, weak robustness, and collisions when robots are traversing. In this paper, we propose an improved A*-based algorithm called EBHSA* algorithm. The EBHSA* algorithm introduces the expansion distance, bidirectional search, heuristic function optimization and smoothing into path planning. The expansion distance extends a certain distance from obstacles to improve path robustness by avoiding collisions. Bidirectional search is a strategy that searches for a path from the start node and from the goal node at the same time. Heuristic function optimization designs a new heuristic function to replace the traditional heuristic function. Smoothing improves path robustness by reducing the number of right-angle turns. Moreover, we carry out simulation tests with the EBHSA* algorithm, and the test results show that the EBHSA* algorithm has excellent performance in terms of robustness and efficiency. In addition, we transplant the EBHSA* algorithm to a robot to verify its effectiveness in the real world.

Emotion-based Parameter Modulation for a Mobile Robot Planning and Control System

<p>The hypothesis that artificial emotion-like mechanisms can improve the adaptive performance of robots and intelligent systems has gained considerable support in recent years. While artificial emotions are typically employed to facilitate human-machine interaction, this thesis instead focuses on modelling emotions and affect in a non-social context. In particular, affective mechanisms are applied to the problem of mobile robot navigation. A three-layered reactive/deliberative controller is developed and implemented, resulting in several contributions to the field of mobile robot control. Rather than employing a reactive layer, a deliberative layer and an interface between them, the control problem is decomposed into three different conceptual spaces - position space, direction space and velocity space - with a distinct control layer applied to each. Existing directional and velocity space approaches such as the vector field histogram (VFH) and dynamic window methods employ different underlying mechanisms and terminology. This thesis unifies these approaches in order to compare and combine them. The weighted sum objective functions employed by some existing approaches that inspired the presented directional and velocity control layers are replaced by weighted products. This enables some hard constraints to be relaxed in favour of weighted contributions, potentially improving a system's flexibility without sacrificing safety (but coming at a cost to efficiency). An affect model is developed that conceptualises emotions and other affective interactions as modulations of cognitive processes. Unlike other models of affect-modulated cognition (e.g. Dorner and Hille, 1995), this model is designed specifically to address problems relating to mobile robot navigation. The role of affect in this model is to continuously adapt a controller's behaviour patterns in response to different environments and momentary conditions encountered by the robot. Affective constructs such as moods and emotions are represented as intensity values that arise from hard-coded interpretations of local stimuli, as well as from learned associations stored in global maps. They are expressed as modulations of control parameters and location-specific biases to path-planning. Extensive simulation experiments are conducted in procedurally-generated environments to assess the performance contributions of this model and its individual components.</p>